Estimation of hemodynamic parameters with deep-learning-based super-resolved 4D flow MRI velocity data

Amirkhosro Kazemi; Sean Callahan; Marcus Stoddard; Amir A. Amini

doi:10.1117/12.2657016

10 April 2023 Estimation of hemodynamic parameters with deep-learning-based super-resolved 4D flow MRI velocity data

Amirkhosro Kazemi, Sean Callahan, Marcus Stoddard, Amir A. Amini

Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 124681H (2023) https://doi.org/10.1117/12.2657016
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

4D Flow Magnetic Resonance Imaging (MRI) allows non-invasive assessment of cardiovascular hemodynamics through the acquisition of three-dimensional pulsatile velocities in a single scan. However, this technique is often plagued by issues of noise and low resolution. In this paper, we employed a deep learning-based super-resolution method utilizing an SR residual network (ResNet) to enhance the measurement of hemodynamic indices at a higher resolution. Our approach enables the derivation of hemodynamic parameters dependent on spatiotemporal velocity derivatives such as vorticity, circulation, and turbulent kinetic energy, which were validated using a phantom model of arterial stenosis. We also compared the deep learning approach with linear, nearest neighbor, and natural interpolation methods with a 2x upsampling factor. The results were evaluated against Computational Fluid Dynamics simulations as a reference and showed that the deep learning approach improved the accuracy of turbulent kinetic energy (TKE) and viscous energy loss at peak systole by 7% and 9%, respectively, indicating a significant enhancement over traditional interpolation methods. Additionally, herein we introduce a novel hemodynamic parameter, enstrophy, as a potential diagnostic biomarker for assessing stenosis severity. Overall, our findings suggest that deep learning is a reliable and efficient approach for predicting hemodynamic parameters from 4Dflow MRI.

Citation Download Citation

Amirkhosro Kazemi, Sean Callahan, Marcus Stoddard, and Amir A. Amini "Estimation of hemodynamic parameters with deep-learning-based super-resolved 4D flow MRI velocity data", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 124681H (10 April 2023); https://doi.org/10.1117/12.2657016

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